class_docstr: This class turns ARIMA model from either the skforecast or pmdarima library 
  into a Forecaster compatible with the skforecast API.
  **New in version 0.10.0**
  
  Parameters
  ----------
  regressor : skforecast.Sarimax.Sarimax, pmdarima.arima.ARIMA
      An ARIMA model instance from either the skforecast or pmdarima library.
  transformer_y : object transformer (preprocessor), default `None`
      An instance of a transformer (preprocessor) compatible with the scikit-learn
      preprocessing API with methods: fit, transform, fit_transform and inverse_transform.
      ColumnTransformers are not allowed since they do not have inverse_transform method.
      The transformation is applied to `y` before training the forecaster. 
  transformer_exog : object transformer (preprocessor), default `None`
      An instance of a transformer (preprocessor) compatible with the scikit-learn
      preprocessing API. The transformation is applied to `exog` before training the
      forecaster. `inverse_transform` is not available when using ColumnTransformers.
  fit_kwargs : dict, default `None`
      Additional arguments to be passed to the `fit` method of the regressor. 
      When using the skforecast Sarimax model, the fit kwargs should be passed 
      using the model parameter `sm_fit_kwargs` and not this one.
  forecaster_id : str, int default `None`
      Name used as an identifier of the forecaster.
  
  Attributes
  ----------
  regressor : skforecast.Sarimax.Sarimax, pmdarima.arima.ARIMA
      An ARIMA model instance from either the skforecast or pmdarima library.
  params: dict
      Parameters of the sarimax model.
  transformer_y : object transformer (preprocessor)
      An instance of a transformer (preprocessor) compatible with the scikit-learn
      preprocessing API with methods: fit, transform, fit_transform and inverse_transform.
      ColumnTransformers are not allowed since they do not have inverse_transform method.
      The transformation is applied to `y` before training the forecaster.
  transformer_exog : object transformer (preprocessor)
      An instance of a transformer (preprocessor) compatible with the scikit-learn
      preprocessing API. The transformation is applied to `exog` before training the
      forecaster. `inverse_transform` is not available when using ColumnTransformers.
  window_size : int
      Not used, present here for API consistency by convention.
  last_window : pandas Series
      Last window the forecaster has seen during training. It stores the
      values needed to predict the next `step` immediately after the training data.
  extended_index : pandas Index
      When predicting using `last_window` and `last_window_exog`, the internal
      statsmodels SARIMAX will be updated using its append method. To do this,
      `last_window` data must start at the end of the index seen by the 
      forecaster, this is stored in forecaster.extended_index.
      Check https://www.statsmodels.org/dev/generated/statsmodels.tsa.arima.model.ARIMAResults.append.html
      to know more about statsmodels append method.
  fitted : bool
      Tag to identify if the regressor has been fitted (trained).
  index_type : type
      Type of index of the input used in training.
  index_freq : str
      Frequency of Index of the input used in training.
  training_range : pandas Index
      First and last values of index of the data used during training.
  included_exog : bool
      If the forecaster has been trained using exogenous variable/s.
  exog_type : type
      Type of exogenous variable/s used in training.
  exog_col_names : list
      Names of columns of `exog` if `exog` used in training was a pandas
      DataFrame.
  fit_kwargs : dict
      Additional arguments to be passed to the `fit` method of the regressor.
  creation_date : str
      Date of creation.
  fit_date : str
      Date of last fit.
  skforecast_version : str
      Version of skforecast library used to create the forecaster.
  python_version : str
      Version of python used to create the forecaster.
  forecaster_id : str, int
      Name used as an identifier of the forecaster.
docstr: Information displayed when a ForecasterSarimax object is printed.
docstr_tok: ["Information","displayed","when","a","ForecasterSarimax","object","is","printed","."]
code: def __repr__(self) -> str:
      """
      Information displayed when a ForecasterSarimax object is printed.
      """
  
      info = (
          f"{'=' * len(type(self).__name__)} \n"
          f"{type(self).__name__} \n"
          f"{'=' * len(type(self).__name__)} \n"
          f"Regressor: {self.regressor} \n"
          f"Regressor parameters: {self.params} \n"
          f"fit_kwargs: {self.fit_kwargs} \n"
          f"Window size: {self.window_size} \n"
          f"Transformer for y: {self.transformer_y} \n"
          f"Transformer for exog: {self.transformer_exog} \n"
          f"Exogenous included: {self.included_exog} \n"
          f"Type of exogenous variable: {self.exog_type} \n"
          f"Exogenous variables names: {self.exog_col_names} \n"
          f"Training range: {self.training_range.to_list() if self.fitted else None} \n"
          f"Training index type: {str(self.index_type).split('.')[-1][:-2] if self.fitted else None} \n"
          f"Training index frequency: {self.index_freq if self.fitted else None} \n"
          f"Creation date: {self.creation_date} \n"
          f"Last fit date: {self.fit_date} \n"
          f"Index seen by the forecaster: {self.extended_index} \n"
          f"Skforecast version: {self.skforecast_version} \n"
          f"Python version: {self.python_version} \n"
          f"Forecaster id: {self.forecaster_id} \n"
      )
  
      return info
  
imports: from typing import Union, Dict, List, Tuple, Any, Optional
  import warnings
  import logging
  import sys
  import numpy
  import pandas
  import pmdarima
  from pmdarima.arima import ARIMA
  from sklearn.base import clone
  from sklearn.exceptions import NotFittedError
  import skforecast
  from ..exceptions import IgnoredArgumentWarning
  from ..utils import check_select_fit_kwargs
  from ..utils import check_y
  from ..utils import check_exog
  from ..utils import check_predict_input
  from ..utils import expand_index
  from ..utils import transform_series
  from ..utils import transform_dataframe
label_desc: Use image node_id 2 for calling the ForecasterSarimax obj's underlying member method code with example usage: obj.__repr__() and returns: info